





"""simple docstring"""





import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      if (
          (cp >= 0x4_e00 and cp <= 0x9_fff)
          or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
          or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
          or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
          or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
          or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
          or (cp >= 0xf_900 and cp <= 0xf_aff)
          or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
      ):  #
            return True

      return False


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      for char in word:
            lowerCAmelCase    		=			ord(SCREAMING_SNAKE_CASE     )
            if not _is_chinese_char(SCREAMING_SNAKE_CASE     ):
                  return 0
      return 1


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			set()

      for token in tokens:
            lowerCAmelCase    		=			len(SCREAMING_SNAKE_CASE     ) > 1 and is_chinese(SCREAMING_SNAKE_CASE     )
            if chinese_word:
                  word_set.add(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			list(SCREAMING_SNAKE_CASE     )
      return word_list


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		set()     ):







      '''simple docstring'''




      if not chinese_word_set:
            return bert_tokens
      lowerCAmelCase    		=			max([len(SCREAMING_SNAKE_CASE     ) for w in chinese_word_set]     )

      lowerCAmelCase    		=			bert_tokens
      lowerCAmelCase		,						lowerCAmelCase    		=			0, len(SCREAMING_SNAKE_CASE     )
      while start < end:
            lowerCAmelCase    		=			True
            if is_chinese(bert_word[start]     ):
                  lowerCAmelCase    		=			min(end - start				,		SCREAMING_SNAKE_CASE     )
                  for i in range(SCREAMING_SNAKE_CASE				,		1				,		-1     ):
                        lowerCAmelCase    		=			"""""".join(bert_word[start : start + i]     )
                        if whole_word in chinese_word_set:
                              for j in range(start + 1				,		start + i     ):
                                    lowerCAmelCase    		=			"""##""" + bert_word[j]
                              lowerCAmelCase    		=			start + i
                              lowerCAmelCase    		=			False
                              break
            if single_word:
                  start += 1
      return bert_word


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]				,		SCREAMING_SNAKE_CASE	:		LTP				,		SCREAMING_SNAKE_CASE	:		BertTokenizer     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[]

      for i in range(0				,		len(SCREAMING_SNAKE_CASE     )				,		1_00     ):
            lowerCAmelCase    		=			ltp_tokenizer.seg(lines[i : i + 1_00]     )[0]
            lowerCAmelCase    		=			[get_chinese_word(SCREAMING_SNAKE_CASE     ) for r in res]
            ltp_res.extend(SCREAMING_SNAKE_CASE     )
      assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			[]
      for i in range(0				,		len(SCREAMING_SNAKE_CASE     )				,		1_00     ):
            lowerCAmelCase    		=			bert_tokenizer(lines[i : i + 1_00]				,		add_special_tokens=SCREAMING_SNAKE_CASE				,		truncation=SCREAMING_SNAKE_CASE				,		max_length=5_12     )
            bert_res.extend(res["""input_ids"""]     )
      assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			[]
      for input_ids, chinese_word in zip(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     ):
            lowerCAmelCase    		=			[]
            for id in input_ids:
                  lowerCAmelCase    		=			bert_tokenizer._convert_id_to_token(SCREAMING_SNAKE_CASE     )
                  input_tokens.append(SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			add_sub_symbol(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )
            lowerCAmelCase    		=			[]
            # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
            for i, token in enumerate(SCREAMING_SNAKE_CASE     ):
                  if token[:2] == "##":
                        lowerCAmelCase    		=			token[2:]
                        # save chinese tokens' pos
                        if len(SCREAMING_SNAKE_CASE     ) == 1 and _is_chinese_char(ord(SCREAMING_SNAKE_CASE     )     ):
                              ref_id.append(SCREAMING_SNAKE_CASE     )
            ref_ids.append(SCREAMING_SNAKE_CASE     )

      assert len(SCREAMING_SNAKE_CASE     ) == len(SCREAMING_SNAKE_CASE     )

      return ref_ids


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]     ):







      '''simple docstring'''




      with open(args.file_name				,		"""r"""				,		encoding="""utf-8"""     ) as f:
            lowerCAmelCase    		=			f.readlines()
      lowerCAmelCase    		=			[line.strip() for line in data if len(SCREAMING_SNAKE_CASE     ) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
      lowerCAmelCase    		=			LTP(args.ltp     )  # faster in GPU device
      lowerCAmelCase    		=			BertTokenizer.from_pretrained(args.bert     )

      lowerCAmelCase    		=			prepare_ref(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      with open(args.save_path				,		"""w"""				,		encoding="""utf-8"""     ) as f:
            lowerCAmelCase    		=			[json.dumps(SCREAMING_SNAKE_CASE     ) + """\n""" for ref in ref_ids]
            f.writelines(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser(description="prepare_chinese_ref")
       parser.add_argument(
           "--file_name",
           type=str,
           default="./resources/chinese-demo.txt",
           help="file need process, same as training data in lm",
       )
       parser.add_argument(
           "--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
       )
       parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
       parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")

       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       main(args)













"""simple docstring"""





from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






